The mortality rate for lung cancer is higher than that for other kinds of cancers around the world. Detection of suspicious lesions in the early stages of cancer can be considered the most effective way to improve survival. Lung nodule detection is one of the more challenging tasks in medical imaging. Lung nodules may be difficult to detect on CT scans because of low contrast, small size, or location of the nodule within an area of complicated anatomy. In particular, it is difficult to detect the extent of a lung nodule purely by inspection, when the nodule is attached to a blood vessel or vessels or surrounded by structure with a similar intensity to the nodule. Detection of the size or extent of a lung nodule is important for accurate diagnosis.
One problem with known techniques is a tendency to include part of a blood vessel with the detected nodule, because of an inability to distinguish between the two.
Another problem with known techniques is that they are over-reliant on the setting of detection parameters by the operator, and there are no ideal parameter values which are applicable to all scan images.
Another problem with known techniques is that they impose a model which is not applicable for all types of lung nodule.
U.S. Pat. No. 4,907,156 discloses a method of detecting lung nodules by pixel thresholding, followed by circularity and/or size testing of contiguous pixels above the threshold.
WO-A-9942950 discloses a method of lung nodule detection involving pre-processing the image to identify candidate nodules, performing image enhancement on a candidate nodule, and identifying whether the candidate nodule is a false positive by obtaining a histogram of accumulated edge gradients as a function of radial angle.
WO-A-02085211 discloses a method of automatic lung nodule detection using adaptive threshold segmentation.
U.S. Pat. No. 6,654,728 discloses a method of discriminating nodules in a chest scan images using fuzzy logic classification.
WO-A-0346831 discloses a computerized lung nodule detection method which creates a mask image by linear interpolation between first and last CT section images, warped to match intermediate CT section images.
The article ‘Automated Lung Nodule Detection at Low-Dose CT: Preliminary Experience’ by Goo JM et. al., Korean J Radiol 4(4), December 2003 discloses a technique for detecting lung nodules using grey-level thresholding and 3D region growing, together with 3D shape analysis to distinguish nodules from blood vessels.